Los sistemas de renta de bicycletas se basan en kioskos que son puestos en diferentes áreas de una ciudad. En estos kioskos las personas pueden suscribirse, rentar y devolver las bicicletas. Esto permite que el usurio rente un bicicleta y la pueda devolver en otro lado. Actualmente hay mas de 500 de estos proyectos alrededor del mundo. Estos kioskos se vuelven sensores del flujo de personas dentro de ciudades.
http://www.academatica.com/econometria1/hour.csv
setwd("/Users/Mrm/Developer/R/Econometria I/Class 2")
bike_df <- read.csv("laboratorio1/hour.csv" , stringsAsFactors = FALSE , strip.white = TRUE , header = TRUE )
str(bike_df)## 'data.frame': 17379 obs. of 17 variables:
## $ instant : int 1 2 3 4 5 6 7 8 9 10 ...
## $ dteday : chr "2011-01-01" "2011-01-01" "2011-01-01" "2011-01-01" ...
## $ season : int 1 1 1 1 1 1 1 1 1 1 ...
## $ yr : int 0 0 0 0 0 0 0 0 0 0 ...
## $ mnth : int 1 1 1 1 1 1 1 1 1 1 ...
## $ hr : int 0 1 2 3 4 5 6 7 8 9 ...
## $ holiday : int 0 0 0 0 0 0 0 0 0 0 ...
## $ weekday : int 6 6 6 6 6 6 6 6 6 6 ...
## $ workingday: int 0 0 0 0 0 0 0 0 0 0 ...
## $ weathersit: int 1 1 1 1 1 2 1 1 1 1 ...
## $ temp : num 0.24 0.22 0.22 0.24 0.24 0.24 0.22 0.2 0.24 0.32 ...
## $ atemp : num 0.288 0.273 0.273 0.288 0.288 ...
## $ hum : num 0.81 0.8 0.8 0.75 0.75 0.75 0.8 0.86 0.75 0.76 ...
## $ windspeed : num 0 0 0 0 0 0.0896 0 0 0 0 ...
## $ casual : int 3 8 5 3 0 0 2 1 1 8 ...
## $ registered: int 13 32 27 10 1 1 0 2 7 6 ...
## $ cnt : int 16 40 32 13 1 1 2 3 8 14 ... demanda_max_mes_1 <- sum(bike_df[bike_df$mnth == 1 , ]$cnt)
demanda_max_mes_2 <- sum(bike_df[bike_df$mnth == 2 , ]$cnt)
demanda_max_mes_3 <- sum(bike_df[bike_df$mnth == 3 , ]$cnt)
demanda_max_mes_4 <- sum(bike_df[bike_df$mnth == 4 , ]$cnt)
demanda_max_mes_5 <- sum(bike_df[bike_df$mnth == 5 , ]$cnt)
demanda_max_mes_6 <- sum(bike_df[bike_df$mnth == 6 , ]$cnt)
demanda_max_mes_7 <- sum(bike_df[bike_df$mnth == 7 , ]$cnt)
demanda_max_mes_8 <- sum(bike_df[bike_df$mnth == 8 , ]$cnt)
demanda_max_mes_9 <- sum(bike_df[bike_df$mnth == 9 , ]$cnt)
demanda_max_mes_10 <- sum(bike_df[bike_df$mnth == 10 , ]$cnt)
demanda_max_mes_11 <- sum(bike_df[bike_df$mnth == 11 , ]$cnt)
demanda_max_mes_12 <- sum(bike_df[bike_df$mnth == 12 , ]$cnt)demanda_max_mes_1## [1] 134933 demanda_max_mes_2## [1] 151352 demanda_max_mes_3## [1] 228920 demanda_max_mes_4## [1] 269094 demanda_max_mes_5## [1] 331686 demanda_max_mes_6## [1] 346342 demanda_max_mes_7## [1] 344948 demanda_max_mes_8## [1] 351194 demanda_max_mes_9## [1] 345991 demanda_max_mes_10## [1] 322352 demanda_max_mes_11## [1] 254831 demanda_max_mes_12## [1] 211036Respuesta : El mes 8 (Agosto)
demanda_max_hora_0 <- sum(bike_df[bike_df$hr == 0 , ]$cnt)
demanda_max_hora_1 <- sum(bike_df[bike_df$hr == 1 , ]$cnt)
demanda_max_hora_2 <- sum(bike_df[bike_df$hr == 2 , ]$cnt)
demanda_max_hora_3 <- sum(bike_df[bike_df$hr == 3 , ]$cnt)
demanda_max_hora_4 <- sum(bike_df[bike_df$hr == 4 , ]$cnt)
demanda_max_hora_5 <- sum(bike_df[bike_df$hr == 5 , ]$cnt)
demanda_max_hora_6 <- sum(bike_df[bike_df$hr == 6 , ]$cnt)
demanda_max_hora_7 <- sum(bike_df[bike_df$hr == 7 , ]$cnt)
demanda_max_hora_8 <- sum(bike_df[bike_df$hr == 8 , ]$cnt)
demanda_max_hora_9 <- sum(bike_df[bike_df$hr == 9 , ]$cnt)
demanda_max_hora_10 <- sum(bike_df[bike_df$hr == 10 , ]$cnt)
demanda_max_hora_11 <- sum(bike_df[bike_df$hr == 11 , ]$cnt)
demanda_max_hora_12 <- sum(bike_df[bike_df$hr == 12 , ]$cnt)
demanda_max_hora_13 <- sum(bike_df[bike_df$hr == 13 , ]$cnt)
demanda_max_hora_14 <- sum(bike_df[bike_df$hr == 14 , ]$cnt)
demanda_max_hora_15 <- sum(bike_df[bike_df$hr == 15 , ]$cnt)
demanda_max_hora_16 <- sum(bike_df[bike_df$hr == 16 , ]$cnt)
demanda_max_hora_17 <- sum(bike_df[bike_df$hr == 17 , ]$cnt)
demanda_max_hora_18 <- sum(bike_df[bike_df$hr == 18 , ]$cnt)
demanda_max_hora_19 <- sum(bike_df[bike_df$hr == 19 , ]$cnt)
demanda_max_hora_20 <- sum(bike_df[bike_df$hr == 20 , ]$cnt)
demanda_max_hora_21 <- sum(bike_df[bike_df$hr == 21 , ]$cnt)
demanda_max_hora_22 <- sum(bike_df[bike_df$hr == 22 , ]$cnt)
demanda_max_hora_23 <- sum(bike_df[bike_df$hr == 23 , ]$cnt) demanda_max_hora_0## [1] 39130 demanda_max_hora_1## [1] 24164 demanda_max_hora_2## [1] 16352 demanda_max_hora_3## [1] 8174 demanda_max_hora_4## [1] 4428 demanda_max_hora_5## [1] 14261 demanda_max_hora_6## [1] 55132 demanda_max_hora_7## [1] 154171 demanda_max_hora_8## [1] 261001 demanda_max_hora_9## [1] 159438 demanda_max_hora_10## [1] 126257 demanda_max_hora_11## [1] 151320 demanda_max_hora_12## [1] 184414 demanda_max_hora_13## [1] 184919 demanda_max_hora_14## [1] 175652 demanda_max_hora_15## [1] 183149 demanda_max_hora_16## [1] 227748 demanda_max_hora_17## [1] 336860 demanda_max_hora_18## [1] 309772 demanda_max_hora_19## [1] 226789 demanda_max_hora_20## [1] 164550 demanda_max_hora_21## [1] 125445 demanda_max_hora_22## [1] 95612 demanda_max_hora_23## [1] 63941Respuesta: El rango de hora es 17:00 a 17:59 horas
demanda_temporatura_1 <- max(bike_df[bike_df$season == 1,]$cnt)
demanda_temporatura_2 <- max(bike_df[bike_df$season == 2,]$cnt)
demanda_temporatura_3 <- max(bike_df[bike_df$season == 3,]$cnt)
demanda_temporatura_4 <- max(bike_df[bike_df$season == 4,]$cnt) demanda_temporatura_1## [1] 801 demanda_temporatura_2## [1] 957 demanda_temporatura_3## [1] 977 demanda_temporatura_4## [1] 967Respuesta: La temporada numero 3 fall
demanda_temp_0.02 <- sum(bike_df[ bike_df$temp == 0.02 , ]$cnt)
demanda_temp_0.04 <- sum(bike_df[ bike_df$temp == 0.04 , ]$cnt)
demanda_temp_0.06 <- sum(bike_df[ bike_df$temp == 0.06 , ]$cnt)
demanda_temp_0.08 <- sum(bike_df[ bike_df$temp == 0.08 , ]$cnt)
demanda_temp_0.10 <- sum(bike_df[ bike_df$temp == 0.10 , ]$cnt)
demanda_temp_0.12 <- sum(bike_df[ bike_df$temp == 0.12 , ]$cnt)
demanda_temp_0.14 <- sum(bike_df[ bike_df$temp == 0.14 , ]$cnt)
demanda_temp_0.16 <- sum(bike_df[ bike_df$temp == 0.16 , ]$cnt)
demanda_temp_0.18 <- sum(bike_df[ bike_df$temp == 0.18 , ]$cnt)
demanda_temp_0.20 <- sum(bike_df[ bike_df$temp == 0.20 , ]$cnt)
demanda_temp_0.22 <- sum(bike_df[ bike_df$temp == 0.22 , ]$cnt)
demanda_temp_0.24 <- sum(bike_df[ bike_df$temp == 0.24 , ]$cnt)
demanda_temp_0.26 <- sum(bike_df[ bike_df$temp == 0.26 , ]$cnt)
demanda_temp_0.28 <- sum(bike_df[ bike_df$temp == 0.28 , ]$cnt)
demanda_temp_0.30 <- sum(bike_df[ bike_df$temp == 0.30 , ]$cnt)
demanda_temp_0.32 <- sum(bike_df[ bike_df$temp == 0.32 , ]$cnt)
demanda_temp_0.34 <- sum(bike_df[ bike_df$temp == 0.34 , ]$cnt)
demanda_temp_0.36 <- sum(bike_df[ bike_df$temp == 0.36 , ]$cnt)
demanda_temp_0.38 <- sum(bike_df[ bike_df$temp == 0.38 , ]$cnt)
demanda_temp_0.40 <- sum(bike_df[ bike_df$temp == 0.40 , ]$cnt)
demanda_temp_0.42 <- sum(bike_df[ bike_df$temp == 0.42 , ]$cnt)
demanda_temp_0.44 <- sum(bike_df[ bike_df$temp == 0.44 , ]$cnt)
demanda_temp_0.46 <- sum(bike_df[ bike_df$temp == 0.46 , ]$cnt)
demanda_temp_0.48 <- sum(bike_df[ bike_df$temp == 0.48 , ]$cnt)
demanda_temp_0.50 <- sum(bike_df[ bike_df$temp == 0.50 , ]$cnt)
demanda_temp_0.52 <- sum(bike_df[ bike_df$temp == 0.52 , ]$cnt)
demanda_temp_0.54 <- sum(bike_df[ bike_df$temp == 0.54 , ]$cnt)
demanda_temp_0.56 <- sum(bike_df[ bike_df$temp == 0.56 , ]$cnt)
demanda_temp_0.58 <- sum(bike_df[ bike_df$temp == 0.58 , ]$cnt)
demanda_temp_0.60 <- sum(bike_df[ bike_df$temp == 0.60 , ]$cnt)
demanda_temp_0.62 <- sum(bike_df[ bike_df$temp == 0.62 , ]$cnt)
demanda_temp_0.64 <- sum(bike_df[ bike_df$temp == 0.64 , ]$cnt)
demanda_temp_0.66 <- sum(bike_df[ bike_df$temp == 0.66 , ]$cnt)
demanda_temp_0.68 <- sum(bike_df[ bike_df$temp == 0.68 , ]$cnt)
demanda_temp_0.70 <- sum(bike_df[ bike_df$temp == 0.70 , ]$cnt)
demanda_temp_0.72 <- sum(bike_df[ bike_df$temp == 0.72 , ]$cnt)
demanda_temp_0.74 <- sum(bike_df[ bike_df$temp == 0.74 , ]$cnt)
demanda_temp_0.76 <- sum(bike_df[ bike_df$temp == 0.76 , ]$cnt)
demanda_temp_0.78 <- sum(bike_df[ bike_df$temp == 0.78 , ]$cnt)
demanda_temp_0.80 <- sum(bike_df[ bike_df$temp == 0.80 , ]$cnt)
demanda_temp_0.82 <- sum(bike_df[ bike_df$temp == 0.82 , ]$cnt)
demanda_temp_0.84 <- sum(bike_df[ bike_df$temp == 0.84 , ]$cnt)
demanda_temp_0.86 <- sum(bike_df[ bike_df$temp == 0.86 , ]$cnt)
demanda_temp_0.88 <- sum(bike_df[ bike_df$temp == 0.88 , ]$cnt)
demanda_temp_0.90 <- sum(bike_df[ bike_df$temp == 0.90 , ]$cnt)
demanda_temp_0.92 <- sum(bike_df[ bike_df$temp == 0.92 , ]$cnt)
demanda_temp_0.94 <- sum(bike_df[ bike_df$temp == 0.94 , ]$cnt)
demanda_temp_0.96 <- sum(bike_df[ bike_df$temp == 0.96 , ]$cnt)
demanda_temp_0.98 <- sum(bike_df[ bike_df$temp == 0.98 , ]$cnt)
demanda_temp_1 <- sum(bike_df[ bike_df$temp == 1 , ]$cnt) demanda_temp_0.02## [1] 712 demanda_temp_0.04## [1] 570 demanda_temp_0.06## [1] 672 demanda_temp_0.08## [1] 480 demanda_temp_0.10## [1] 2514 demanda_temp_0.12## [1] 4440 demanda_temp_0.14## [1] 7605 demanda_temp_0.16## [1] 15083 demanda_temp_0.18## [1] 9318 demanda_temp_0.20## [1] 28230 demanda_temp_0.22## [1] 29434 demanda_temp_0.24## [1] 41843 demanda_temp_0.26## [1] 49170 demanda_temp_0.28## [1] 32132 demanda_temp_0.30## [1] 74303 demanda_temp_0.32## [1] 82015 demanda_temp_0.34## [1] 87274 demanda_temp_0.36## [1] 99202 demanda_temp_0.38## [1] 61087 demanda_temp_0.40## [1] 102809 demanda_temp_0.42## [1] 96087 demanda_temp_0.44## [1] 80566 demanda_temp_0.46## [1] 91065 demanda_temp_0.48## [1] 54845 demanda_temp_0.50## [1] 105366 demanda_temp_0.52## [1] 112850 demanda_temp_0.54## [1] 113962 demanda_temp_0.56## [1] 123756 demanda_temp_0.58## [1] 67730 demanda_temp_0.60## [1] 149905 demanda_temp_0.62## [1] 148185 demanda_temp_0.64## [1] 154985 demanda_temp_0.66## [1] 156204 demanda_temp_0.68## [1] 73129 demanda_temp_0.70## [1] 177298 demanda_temp_0.72## [1] 163449 demanda_temp_0.74## [1] 161587 demanda_temp_0.76## [1] 135660 demanda_temp_0.78## [1] 52930 demanda_temp_0.80## [1] 112897 demanda_temp_0.82## [1] 72354 demanda_temp_0.84## [1] 44963 demanda_temp_0.86## [1] 42307 demanda_temp_0.88## [1] 19274 demanda_temp_0.90## [1] 27836 demanda_temp_0.92## [1] 15681 demanda_temp_0.94## [1] 3690 demanda_temp_0.96## [1] 4392 demanda_temp_0.98## [1] 539 demanda_temp_1## [1] 294Respuesta , la demanda baja despues de .70 Celcious
humedad_0.00 <- sum(bike_df[ bike_df$temp == 0.00 , ]$cnt)
humedad_0.01 <- sum(bike_df[ bike_df$temp == 0.01 , ]$cnt)
humedad_0.02 <- sum(bike_df[ bike_df$temp == 0.02 , ]$cnt)
humedad_0.03 <- sum(bike_df[ bike_df$temp == 0.03 , ]$cnt)
humedad_0.04 <- sum(bike_df[ bike_df$temp == 0.04 , ]$cnt)
humedad_0.05 <- sum(bike_df[ bike_df$temp == 0.05 , ]$cnt)
humedad_0.06 <- sum(bike_df[ bike_df$temp == 0.06 , ]$cnt)
humedad_0.07 <- sum(bike_df[ bike_df$temp == 0.07 , ]$cnt)
humedad_0.08 <- sum(bike_df[ bike_df$temp == 0.08 , ]$cnt)
humedad_0.09 <- sum(bike_df[ bike_df$temp == 0.09 , ]$cnt)
humedad_0.10 <- sum(bike_df[ bike_df$temp == 0.10 , ]$cnt)
humedad_0.11 <- sum(bike_df[ bike_df$temp == 0.11 , ]$cnt)
humedad_0.12 <- sum(bike_df[ bike_df$temp == 0.12 , ]$cnt)
humedad_0.13 <- sum(bike_df[ bike_df$temp == 0.13 , ]$cnt)
humedad_0.14 <- sum(bike_df[ bike_df$temp == 0.14 , ]$cnt)
humedad_0.15 <- sum(bike_df[ bike_df$temp == 0.15 , ]$cnt)
humedad_0.16 <- sum(bike_df[ bike_df$temp == 0.16 , ]$cnt)
humedad_0.17 <- sum(bike_df[ bike_df$temp == 0.17 , ]$cnt)
humedad_0.18 <- sum(bike_df[ bike_df$temp == 0.18 , ]$cnt)
humedad_0.19 <- sum(bike_df[ bike_df$temp == 0.19 , ]$cnt)
humedad_0.20 <- sum(bike_df[ bike_df$temp == 0.20 , ]$cnt)
humedad_0.21 <- sum(bike_df[ bike_df$temp == 0.21 , ]$cnt)
humedad_0.22 <- sum(bike_df[ bike_df$temp == 0.22 , ]$cnt)
humedad_0.23 <- sum(bike_df[ bike_df$temp == 0.23 , ]$cnt)
humedad_0.24 <- sum(bike_df[ bike_df$temp == 0.24 , ]$cnt)
humedad_0.25 <- sum(bike_df[ bike_df$temp == 0.25 , ]$cnt)
humedad_0.26 <- sum(bike_df[ bike_df$temp == 0.26 , ]$cnt)
humedad_0.27 <- sum(bike_df[ bike_df$temp == 0.27 , ]$cnt)
humedad_0.28 <- sum(bike_df[ bike_df$temp == 0.28 , ]$cnt)
humedad_0.29 <- sum(bike_df[ bike_df$temp == 0.29 , ]$cnt)
humedad_0.30 <- sum(bike_df[ bike_df$temp == 0.30 , ]$cnt)
humedad_0.31 <- sum(bike_df[ bike_df$temp == 0.31 , ]$cnt)
humedad_0.32 <- sum(bike_df[ bike_df$temp == 0.32 , ]$cnt)
humedad_0.33 <- sum(bike_df[ bike_df$temp == 0.33 , ]$cnt)
humedad_0.34 <- sum(bike_df[ bike_df$temp == 0.34 , ]$cnt)
humedad_0.35 <- sum(bike_df[ bike_df$temp == 0.35 , ]$cnt)
humedad_0.36 <- sum(bike_df[ bike_df$temp == 0.36 , ]$cnt)
humedad_0.37 <- sum(bike_df[ bike_df$temp == 0.37 , ]$cnt)
humedad_0.38 <- sum(bike_df[ bike_df$temp == 0.38 , ]$cnt)
humedad_0.39 <- sum(bike_df[ bike_df$temp == 0.39 , ]$cnt)
humedad_0.40 <- sum(bike_df[ bike_df$temp == 0.40 , ]$cnt)
humedad_0.41 <- sum(bike_df[ bike_df$temp == 0.41 , ]$cnt)
humedad_0.42 <- sum(bike_df[ bike_df$temp == 0.42 , ]$cnt)
humedad_0.43 <- sum(bike_df[ bike_df$temp == 0.43 , ]$cnt)
humedad_0.44 <- sum(bike_df[ bike_df$temp == 0.44 , ]$cnt)
humedad_0.45 <- sum(bike_df[ bike_df$temp == 0.45 , ]$cnt)
humedad_0.46 <- sum(bike_df[ bike_df$temp == 0.46 , ]$cnt)
humedad_0.47 <- sum(bike_df[ bike_df$temp == 0.47 , ]$cnt)
humedad_0.48 <- sum(bike_df[ bike_df$temp == 0.48 , ]$cnt)
humedad_0.49 <- sum(bike_df[ bike_df$temp == 0.49 , ]$cnt)
humedad_0.50 <- sum(bike_df[ bike_df$temp == 0.50 , ]$cnt)
humedad_0.51 <- sum(bike_df[ bike_df$temp == 0.51 , ]$cnt)
humedad_0.52 <- sum(bike_df[ bike_df$temp == 0.52 , ]$cnt)
humedad_0.53 <- sum(bike_df[ bike_df$temp == 0.53 , ]$cnt)
humedad_0.54 <- sum(bike_df[ bike_df$temp == 0.54 , ]$cnt)
humedad_0.55 <- sum(bike_df[ bike_df$temp == 0.55 , ]$cnt)
humedad_0.56 <- sum(bike_df[ bike_df$temp == 0.56 , ]$cnt)
humedad_0.57 <- sum(bike_df[ bike_df$temp == 0.57 , ]$cnt)
humedad_0.58 <- sum(bike_df[ bike_df$temp == 0.58 , ]$cnt)
humedad_0.59 <- sum(bike_df[ bike_df$temp == 0.59 , ]$cnt)
humedad_0.60 <- sum(bike_df[ bike_df$temp == 0.60 , ]$cnt)
humedad_0.61 <- sum(bike_df[ bike_df$temp == 0.61 , ]$cnt)
humedad_0.62 <- sum(bike_df[ bike_df$temp == 0.62 , ]$cnt)
humedad_0.63 <- sum(bike_df[ bike_df$temp == 0.63 , ]$cnt)
humedad_0.64 <- sum(bike_df[ bike_df$temp == 0.64 , ]$cnt)
humedad_0.65 <- sum(bike_df[ bike_df$temp == 0.65 , ]$cnt)
humedad_0.66 <- sum(bike_df[ bike_df$temp == 0.66 , ]$cnt)
humedad_0.67 <- sum(bike_df[ bike_df$temp == 0.67 , ]$cnt)
humedad_0.68 <- sum(bike_df[ bike_df$temp == 0.68 , ]$cnt)
humedad_0.69 <- sum(bike_df[ bike_df$temp == 0.69 , ]$cnt)
humedad_0.70 <- sum(bike_df[ bike_df$temp == 0.70 , ]$cnt)
humedad_0.71 <- sum(bike_df[ bike_df$temp == 0.71 , ]$cnt)
humedad_0.72 <- sum(bike_df[ bike_df$temp == 0.72 , ]$cnt)
humedad_0.73 <- sum(bike_df[ bike_df$temp == 0.73 , ]$cnt)
humedad_0.74 <- sum(bike_df[ bike_df$temp == 0.74 , ]$cnt)
humedad_0.75 <- sum(bike_df[ bike_df$temp == 0.75 , ]$cnt)
humedad_0.76 <- sum(bike_df[ bike_df$temp == 0.76 , ]$cnt)
humedad_0.77 <- sum(bike_df[ bike_df$temp == 0.77 , ]$cnt)
humedad_0.78 <- sum(bike_df[ bike_df$temp == 0.78 , ]$cnt)
humedad_0.79 <- sum(bike_df[ bike_df$temp == 0.79 , ]$cnt)
humedad_0.80 <- sum(bike_df[ bike_df$temp == 0.80 , ]$cnt)
humedad_0.81 <- sum(bike_df[ bike_df$temp == 0.81 , ]$cnt)
humedad_0.82 <- sum(bike_df[ bike_df$temp == 0.82 , ]$cnt)
humedad_0.83 <- sum(bike_df[ bike_df$temp == 0.83 , ]$cnt)
humedad_0.84 <- sum(bike_df[ bike_df$temp == 0.84 , ]$cnt)
humedad_0.85 <- sum(bike_df[ bike_df$temp == 0.85 , ]$cnt)
humedad_0.86 <- sum(bike_df[ bike_df$temp == 0.86 , ]$cnt)
humedad_0.87 <- sum(bike_df[ bike_df$temp == 0.87 , ]$cnt)
humedad_0.88 <- sum(bike_df[ bike_df$temp == 0.88 , ]$cnt)
humedad_0.89 <- sum(bike_df[ bike_df$temp == 0.89 , ]$cnt)
humedad_0.90 <- sum(bike_df[ bike_df$temp == 0.90 , ]$cnt)
humedad_0.91 <- sum(bike_df[ bike_df$temp == 0.91 , ]$cnt)
humedad_0.92 <- sum(bike_df[ bike_df$temp == 0.92 , ]$cnt)
humedad_0.93 <- sum(bike_df[ bike_df$temp == 0.93 , ]$cnt)
humedad_0.94 <- sum(bike_df[ bike_df$temp == 0.94 , ]$cnt)
humedad_0.95 <- sum(bike_df[ bike_df$temp == 0.95 , ]$cnt)
humedad_0.96 <- sum(bike_df[ bike_df$temp == 0.96 , ]$cnt)
humedad_0.97 <- sum(bike_df[ bike_df$temp == 0.97 , ]$cnt)
humedad_0.98 <- sum(bike_df[ bike_df$temp == 0.98 , ]$cnt)
humedad_0.99 <- sum(bike_df[ bike_df$temp == 0.99 , ]$cnt)
humedad_1 <- sum(bike_df[ bike_df$temp == 1.00 , ]$cnt) humedad_0.01## [1] 0 humedad_0.02## [1] 712 humedad_0.03## [1] 0 humedad_0.04## [1] 570 humedad_0.05## [1] 0 humedad_0.06## [1] 672 humedad_0.07## [1] 0 humedad_0.08 ## [1] 480 humedad_0.09## [1] 0 humedad_0.10## [1] 2514 humedad_0.11## [1] 0 humedad_0.12## [1] 4440 humedad_0.13## [1] 0 humedad_0.14## [1] 7605 humedad_0.15## [1] 0 humedad_0.16## [1] 15083 humedad_0.17## [1] 0 humedad_0.18## [1] 9318 humedad_0.19## [1] 0 humedad_0.20## [1] 28230 humedad_0.21## [1] 0 humedad_0.22## [1] 29434 humedad_0.23## [1] 0 humedad_0.24## [1] 41843 humedad_0.25## [1] 0 humedad_0.26## [1] 49170 humedad_0.27## [1] 0 humedad_0.28## [1] 32132 humedad_0.29## [1] 0 humedad_0.30## [1] 74303 humedad_0.31## [1] 0 humedad_0.32## [1] 82015 humedad_0.33## [1] 0 humedad_0.34## [1] 87274 humedad_0.35## [1] 0 humedad_0.36## [1] 99202 humedad_0.37## [1] 0 humedad_0.38## [1] 61087 humedad_0.39## [1] 0 humedad_0.40## [1] 102809 humedad_0.41## [1] 0 humedad_0.42## [1] 96087 humedad_0.43## [1] 0 humedad_0.44## [1] 80566 humedad_0.45## [1] 0 humedad_0.46## [1] 91065 humedad_0.47## [1] 0 humedad_0.48## [1] 54845 humedad_0.49## [1] 0 humedad_0.50## [1] 105366 humedad_0.51## [1] 0 humedad_0.52## [1] 112850 humedad_0.53## [1] 0 humedad_0.54## [1] 113962 humedad_0.55## [1] 0 humedad_0.56## [1] 123756 humedad_0.57## [1] 0 humedad_0.58## [1] 67730 humedad_0.59## [1] 0 humedad_0.60## [1] 149905 humedad_0.61## [1] 0 humedad_0.62## [1] 148185 humedad_0.63## [1] 0 humedad_0.64## [1] 154985 humedad_0.65## [1] 0 humedad_0.66## [1] 156204 humedad_0.67## [1] 0 humedad_0.68## [1] 73129 humedad_0.69## [1] 0 humedad_0.70## [1] 177298 humedad_0.71## [1] 0 humedad_0.72## [1] 163449 humedad_0.73## [1] 0 humedad_0.74## [1] 161587 humedad_0.75## [1] 0 humedad_0.76## [1] 135660 humedad_0.77## [1] 0 humedad_0.78## [1] 52930 humedad_0.79## [1] 0 humedad_0.80## [1] 112897 humedad_0.81## [1] 0 humedad_0.82## [1] 72354 humedad_0.83## [1] 0 humedad_0.84## [1] 44963 humedad_0.85## [1] 0 humedad_0.86## [1] 42307 humedad_0.87## [1] 0 humedad_0.88## [1] 19274 humedad_0.89## [1] 0 humedad_0.90## [1] 27836 humedad_0.91## [1] 0 humedad_0.92## [1] 15681 humedad_0.93## [1] 0 humedad_0.94## [1] 3690 humedad_0.95## [1] 0 humedad_0.96## [1] 4392 humedad_0.97## [1] 0 humedad_0.98## [1] 539 humedad_0.99## [1] 0 humedad_1## [1] 294Respuesta : Con una humedad > 0.56 la demanda tiende a bajar, excluimos los valores atipicos.
Las condiciones ideales se cumplen basado en las siguientes condiciones: